<html>
  <head>
    <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
    <title>mvvacov</title>
  </head>
  <body bgcolor="#FFFFFF">
    <center></center>
    <div align="right">Last update : May 2002</div>
    <p>
      <b>mvvacov</b> -   computes  variance-covariance matrix</p>
    <h3>
      <font color="blue">Calling Sequence</font>
    </h3>
    <dl>
      <dd>
        <tt>v=mvvacov(x)  </tt>
      </dd>
    </dl>
    <h3>
      <font color="blue">Parameters</font>
    </h3>
    <ul>
      <li>
        <tt>
          <b>x</b>
        </tt>: real or complex vector or matrix</li>
    </ul>
    <h3>
      <font color="blue">Description</font>
    </h3>
    <p>
    This    function   computes    v,    the   matrix    of
    variance-covariance  of   the  "tableau"  x   (x  is  a
    numerical  matrix  nxp)  who  gives  the  values  of  p
    variables for n individuals: the (i,j) coefficient of v
    is v(i,j)=E(xi-xibar)(xj-xjbar),  where E is  the first
    moment of a variable, xi is the i-th variable and xibar
    the mean of the xi variable.</p>
    <h3>
      <font color="blue">Examples</font>
    </h3>
    <pre>


x=[0.2113249 0.0002211 0.6653811;0.7560439 0.4453586 0.6283918]
v=mvvacov(x)
 
  </pre>
    <h3>
      <font color="blue">Author</font>
    </h3>
    <p> Carlos Klimann</p>
    <h3>
      <font color="blue">Bibliography</font>
    </h3>
    <p>
        Saporta, Gilbert, Probabilites,  Analyse des Donnees et Statistique, Editions Technip, Paris, 1990.  Mardia,  K.V., Kent,  J.T. &amp;  Bibby,  J.M., Multivariate Analysis, Academic Press, 1979.</p>
  </body>
</html>
